Semi-automatic Method Research Articles

Meso-scale seabed quantification with geoacoustic inversion

Knowledge of sub-seabed geoacoustic properties, for example depth dependent sound speed and porosity, is of importance for a variety of applications. Here, we present a semi-automated geoacoustic inversion method for autonomous underwater vehicle data that objectively adapts model inference to seabed structure. Through parallelized trans-dimensional Bayesian inference, we infer seabed properties along a 12 km survey track on the scale of about 10 cm and 50 m in the vertical and horizontal, respectively. The inferred seabed properties include sound speed, attenuation, density, and porosity as a function of depth from acoustic reflection coefficient data. Parameter uncertainties are quantified, and the seabed properties agree closely with core samples at two control points and the layering structure with an independent sub-bottom seismic survey. Recovering high resolution seabed properties over large areas is shown to be feasible, which could become an important tool for marine industries, navies and oceanic research organizations.

Myosteatosis and aortic calcium score on abdominal CT as prognostic markers in non-dialysis chronic kidney disease patients

We aimed to examine the relationship between abdominal computed tomography (CT)-based body composition data and both renal function decline and all-cause mortality in patients with non-dialysis chronic kidney disease (CKD). This retrospective study comprised non-dialysis CKD patients who underwent consecutive unenhanced abdominal CT between January 2010 and December 2011. CT-based body composition was measured using semiautomated method that included visceral fat, subcutaneous fat, skeletal muscle area and density, and abdominal aortic calcium score (AAS). Sarcopenia and myosteatosis were defined by decreased skeletal muscle index (SMI) and decreased skeletal muscle density, respectively, each with specific cutoffs. Risk factors for CKD progression and survival were identified using logistic regression and Cox proportional hazard regression models. Survival between groups based on myosteatosis and AAS was compared using the Kaplan–Meier curve. 149 patients (median age: 70 years) were included; 79 (53.0%) patients had sarcopenia and 112 (75.2%) had myosteatosis. The median AAS was 560.9 (interquartile range: 55.7–1478.3)/m2. The prognostic factors for CKD progression were myosteatosis [odds ratio (OR) = 4.31, p = 0.013] and high AAS (OR = 1.03, p = 0.001). Skeletal muscle density [hazard ratio (HR) = 0.93, p = 0.004] or myosteatosis (HR = 4.87, p = 0.032) and high AAS (HR = 1.02, p = 0.001) were independent factors for poor survival outcomes. The presence of myosteatosis and the high burden of aortic calcium were significant factors for CKD progression and survival in patients with non-dialysis CKD.

A Growth Prediction Model of Pulmonary Ground-Glass Nodules Based on Clinical Visualization Parameters

Objective To establish a model for predicting the growth of pulmonary ground-glass nodules (GGN) based on the clinical visualization parameters extracted by the 3D reconstruction technique and to verify the prediction performance of the model. Methods A retrospective analysis was carried out for 354 cases of pulmonary GGN followed up regularly in the outpatient of pulmonary nodules in Zhoushan Hospital of Zhejiang Province from March 2015 to December 2022.The semi-automatic segmentation method of 3D Slicer was employed to extract the quantitative imaging features of nodules.According to the follow-up results,the nodules were classified into a resting group and a growing group.Furthermore,the nodules were classified into a training set and a test set by the simple random method at a ratio of 7∶3.Clinical and imaging parameters were used to establish a prediction model,and the prediction performance of the model was tested on the validation set. Results A total of 119 males and 235 females were included,with a median age of 55.0 (47.0,63.0) years and the mean follow-up of (48.4±16.3) months.There were 247 cases in the training set and 107 cases in the test set.The binary Logistic regression analysis showed that age (95%CI=1.010-1.092,P=0.015) and mass (95%CI=1.002-1.067,P=0.035) were independent predictors of nodular growth.The mass (M) of nodules was calculated according to the formula M=V×(CTmean+1000)×0.001 (where V is the volume,V=3/4πR3,R:radius).Therefore,the logit prediction model was established as ln[P/(1-P)]=-1.300+0.043×age+0.257×two-dimensional diameter+0.007×CTmean.The Hosmer-Lemeshow goodness of fit test was performed to test the fitting degree of the model for the measured data in the validation set (χ2=4.515,P=0.808).The check plot was established for the prediction model,which showed the area under receiver-operating characteristic curve being 0.702. Conclusions The results of this study indicate that patient age and nodule mass are independent risk factors for promoting the growth of pulmonary GGN.A model for predicting the growth possibility of GGN is established and evaluated,which provides a basis for the formulation of GGN management strategies.

Estimation of EIA by Semi-Automated Direct Method for Western Indian Urban Catchment Area

Urbanization impacts water resources, with impervious surfaces indicating the degree of disturbance. Effective impervious area (EIA) is a better measure than total impervious area (TIA) for urban runoff. EIA is the portion of TIA connected to the storm sewer system. Remote sensing data alone is insufficient for EIA determination. An alternative approach integrates remote sensing data with drainage networks to ensure connectivity of impervious surfaces. This approach uses GIS data including multi-sensor data. The modified literature algorithm was used in python to analyze the study area. It is observed that for western Indian urban catchment area, some empirical equation such as Sutherland’s and Alley-Veenhuis showed under-estimation and over-estimation at some watershed parts which were obtained using DEM delineation. Based on the results obtained from proposed method, a quadratic relationship was established between easily measured TIA and hydraulically relevant EIA for an urban catchment in western India. These outcomes should result in more effective and properly designed SCMs, with potential improvements in water quality and cost savings.

Development and validation of a semi-automated and unsupervised method for femur segmentation from CT

Quantitative computed tomography (QCT)-based in silico models have demonstrated improved accuracy in predicting hip fractures with respect to the current gold standard, the areal bone mineral density. These models require that the femur bone is segmented as a first step. This task can be challenging, and in fact, it is often almost fully manual, which is time-consuming, operator-dependent, and hard to reproduce. This work proposes a semi-automated procedure for femur bone segmentation from CT images. The proposed procedure is based on the bone and joint enhancement filter and graph-cut algorithms. The semi-automated procedure performances were assessed on 10 subjects through comparison with the standard manual segmentation. Metrics based on the femur geometries and the risk of fracture assessed in silico resulting from the two segmentation procedures were considered. The average Hausdorff distance (0.03 ± 0.01 mm) and the difference union ratio (0.06 ± 0.02) metrics computed between the manual and semi-automated segmentations were significantly higher than those computed within the manual segmentations (0.01 ± 0.01 mm and 0.03 ± 0.02). Besides, a blind qualitative evaluation revealed that the semi-automated procedure was significantly superior (p < 0.001) to the manual one in terms of fidelity to the CT. As for the hip fracture risk assessed in silico starting from both segmentations, no significant difference emerged between the two (R2 = 0.99). The proposed semi-automated segmentation procedure overcomes the manual one, shortening the segmentation time and providing a better segmentation. The method could be employed within CT-based in silico methodologies and to segment large volumes of images to train and test fully automated and supervised segmentation methods.

A semi-automatic method for extracting mitochondrial cristae characteristics from 3D focused ion beam scanning electron microscopy data

Mitochondria are the main suppliers of energy for cells and their bioenergetic function is regulated by mitochondrial dynamics: the constant changes in mitochondria size, shape, and cristae morphology to secure cell homeostasis. Although changes in mitochondrial function are implicated in a wide range of diseases, our understanding is challenged by a lack of reliable ways to extract spatial features from the cristae, the detailed visualization of which requires electron microscopy (EM). Here, we present a semi-automatic method for the segmentation, 3D reconstruction, and shape analysis of mitochondria, cristae, and intracristal spaces based on 2D EM images of the murine hippocampus. We show that our method provides a more accurate characterization of mitochondrial ultrastructure in 3D than common 2D approaches and propose an operational index of mitochondria’s internal organization. With an improved consistency of 3D shape analysis and a decrease in the workload needed for large-scale analysis, we speculate that this tool will help increase our understanding of mitochondrial dynamics in health and disease.

NOVEL ECHOCARDIOGRAM ANALYSIS OF CARDIAC DYSFUNCTION IS ASSOCIATED WITH MORTALITY IN PEDIATRIC SEPSIS.

Objectives: The objective of our study was to semiautomatically generate echocardiogram indices in pediatric sepsis using novel algorithms and determine which indices were associated with mortality. We hypothesized that strain and diastolic indices would be most associated with mortality. Design: Retrospective cohort study of children with sepsis from 2017 to 2022. Survivors and nonsurvivors were compared for echocardiogram indices. Multivariate Cox proportional hazard models were constructed for our primary outcome of in-hospital mortality. Linear regression was performed for secondary outcomes, which included multiple composite 28-day outcomes. Results: Of the 54 patients in the study, 9 (17%) died. Multiple echocardiogram indices of both right (RV) and left ventricles (LV) were associated with in-hospital mortality [RV GLS adjusted hazard ratio (aHR): 1.16 (1.03-1.29), P = 0.011; RV global longitudinal early diastolic strain rate (GLSre) aHR: 0.24 (0.07 to 0.75), P = 0.014; LV GLSre aHR: 0.33 (0.11-0.97), P = 0.044]. Impairment in GLS was associated with fewer ventilator-free days [RV GLS β-coefficient: -0.47 (-0.84 to -0.10), P = 0.013; LV GLS β-coefficient -0.62 (-1.07 to -0.17), P = 0.008], organ-support free days [RV GLS β-coefficient: -0.49 (-0.87 to -0.11), P = 0.013; LV GLS β-coefficient: -0.64 (-1.10 to -0.17), P = 0.008], and days free from ICU [RV GLS β-coefficient: -0.42 (-0.79 to -0.05), P = 0.026; LV GLS β-coefficient: -0.58 (-1.03 to -0.13), P = 0.012]. Systolic indices were not associated with mortality in this cohort. Conclusion: Our study demonstrates the feasibility of obtaining echocardiogram indices in a semiautomatic method using our algorithms. We showed that abnormal strain is associated with worse outcomes in a cohort of children with sepsis.

NaRuto: Automatically Acquiring Planning Models from Narrative Texts

Domain model acquisition has been identified as a bottleneck in the application of planning technology, especially within narrative planning. Learning action models from narrative texts in an automated way is essential to overcome this barrier, but challenging because of the inherent complexities of such texts. We present an evaluation of planning domain models derived from narrative texts using our fully automated, unsupervised system, NaRuto. Our system combines structured event extraction, predictions of commonsense event relations, and textual contradictions and similarities. Evaluation results show that NaRuto generates domain models of significantly better quality than existing fully automated methods, and even sometimes on par with those created by semi-automated methods, with human assistance.

IDet3D: Towards Efficient Interactive Object Detection for LiDAR Point Clouds

Accurately annotating multiple 3D objects in LiDAR scenes is laborious and challenging. While a few previous studies have attempted to leverage semi-automatic methods for cost-effective bounding box annotation, such methods have limitations in efficiently handling numerous multi-class objects. To effectively accelerate 3D annotation pipelines, we propose iDet3D, an efficient interactive 3D object detector. Supporting a user-friendly 2D interface, which can ease the cognitive burden of exploring 3D space to provide click interactions, iDet3D enables users to annotate the entire objects in each scene with minimal interactions. Taking the sparse nature of 3D point clouds into account, we design a negative click simulation (NCS) to improve accuracy by reducing false-positive predictions. In addition, iDet3D incorporates two click propagation techniques to take full advantage of user interactions: (1) dense click guidance (DCG) for keeping user-provided information throughout the network and (2) spatial click propagation (SCP) for detecting other instances of the same class based on the user-specified objects. Through our extensive experiments, we present that our method can construct precise annotations in a few clicks, which shows the practicality as an efficient annotation tool for 3D object detection.

Merging galaxies in isolated environments. I. Multiband photometry, classification, stellar masses, and star formation rates

Extragalactic surveys provide significant statistical data for the study of crucial galaxy parameters (e.g. stellar mass, M$_*$, and star formation rate ,SFR) used to constrain galaxy evolution under different environmental conditions. These quantities are derived using manual or automatic methods for galaxy detection and flux measurement in imaging data at different wavelengths. The reliability of these automatic measurements, however, is subject to mis-identification and poor fitting due to the morphological irregularities present in resolved nearby galaxies (e.g. clumps, tidal disturbances, star- forming regions) and its environment (galaxies in overlap). Our aim is to provide accurate multi-wavelength photometry (from the UV to the IR, including GALEX, SDSS, and WISE) in a sample of $ 600$ nearby (z&lt;0.1) isolated mergers, as well as estimations of M$_*$ and SFR. We performed photometry following a semi-automated approach using SExtractor, confirming by visual inspection that we successfully extracted the light from the entire galaxy, including tidal tails and star-forming regions. We used the available SED fitting code MAGPHYS in order to estimate M$_*$ and SFR. We provide the first catalogue of isolated merging galaxies of galaxy mergers including aperture-corrected photometry in 11 bands (FUV, NUV, u, g, r, i, z, W1, W2, W3, and W4), morphological classification, merging stage, M$_*$, and SFR. We found that SFR and M$_*$ derived from automated catalogues can be wrong by up to three orders of magnitude as a result of incorrect photometry. Contrary to previous methods, our semi-automated method can reliably extract the flux of a merging system completely. Even when the SED fitting often smooths out some of the differences in the photometry, caution using automatic photometry is suggested as these measurements can lead to large differences in M$_*$ and SFR estimations.

Abstract 3529: Leveraging deep learning for fully automated analysis of pre-clinical mouse positron emission tomography

Abstract Turning early-cancer diagnosis into actionable treatment often relies on highly accurate and sensitive imaging techniques to guide surgical intervention or to monitor therapeutic efficacy. Earli is developing a highly sensitive, orthogonal approach that uses genetic constructs to usurp dysregulated cancer pathways to force the tumor to produce a PET reporter gene, enabling the use of radiotracers amenable to positron emission tomography/computed tomography (PET/CT) to guide precision imaging. However, analysis of PET/CT images using standard manual and semi-automated methods is challenging resulting in reduced experimental throughput and user variability, highlighting the need for a fully automated PET/CT analysis pipeline using Deep Learning (DL). To increase pre-clinical throughput and reduce inter-user variability, we developed an automated DL processing pipeline in Python to perform three major tasks: separation of multi-mouse bed data for PET uptake quantification by mouse, segmentation of tumors and background organs, and quantification of PET signal for PK/PD analysis. Preclinical PET/CT data was acquired using a 4 animal, multi-mouse bed and separated into individual mice by co-registering the reconstructed CT image against a multi-mouse bed reference mask using a rigid 3D Euler transformation algorithm and cropping at fixed indices. Following mouse separation, a 3D nnUNet architecture is used to perform semantic segmentation of regions of interest (ROI) on CT for bi-hemispheric subcutaneous tumors, lungs, liver, kidneys, spleen, and the bladder. nnUNet was trained on PET/CT of 311 mice consisting of a mixture of naïve and tumor-bearing animals subcutaneously implanted with H1299 cells using a five-fold cross-validation strategy with 1000 epochs per fold. Model performance was evaluated by comparing the network prediction to the reference manually annotated masks using the Dice coefficient. PET uptake is reported as percent injected dose per milliliter (%ID/mL) or standardized uptake value (SUV). Segmentation performance results for the hold-out set identified mean Dice scores greater than 0.80 for all ROIs, reflecting the similarity between model predictions and human annotations. The automated pipeline reduced the analysis time from 5.5-6 hours per mouse when using traditional manual/semi-automatic approaches to approximately 15 minutes. In summary, we developed a fully automated DL-based PET/CT quantification pipeline for pre-clinical mouse studies that provides accurate ROI segmentation and PET uptake quantification, significantly increasing experimental throughput and reducing inter-user variability. Further improvements to model inference include loss functions weighted for poor performing ROIs and addition of lung nodule segmentation for eventual translation to humans as an accompaniment to Earli’s theranostic programs. Citation Format: Hung-Yu Henry Lee, Mohammed Goryawala, Tim Sproul, Maggie Louie, David Suhy. Leveraging deep learning for fully automated analysis of pre-clinical mouse positron emission tomography [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3529.

Semiautomated MRI-Based Method for Orbital Volume and Contour Analysis.

The architecture of the orbital cavity is intricate, and precise measurement of its growth is essential for managing ocular and orbital pathologies. Most methods for those measurements are by CT imaging, although MRI for soft tissue assessment is indicated in many cases, specifically pediatric patients. This study introduces a novel semiautomated MRI-based approach for depicting orbital shape and dimensions. A retrospective cohort study. Patients with at least 1 normal orbit who underwent both CT and MRI imaging at a single center from 2015 to 2023. Orbital dimensions included volume, horizontal and vertical lengths, and depth. These were determined by manual segmentation followed by 3-dimensional image processing software. Differences in orbital measurements between MRI and CT scans. Thirty-one patients (mean age 47.7 ± 23.8 years, 21 [67.7%]) females, were included. The mean differences in delta values between orbital measurements on CT versus MRI were: volume 0.03 ± 2.01 ml, horizontal length 0.53 ± 2.12 mm, vertical length, 0.36 ± 2.53 mm, and depth 0.97 ± 3.90 mm. The CT and. MRI orbital measurements were strongly correlated: volume (r = 0.92, p < 0.001), horizontal length (r = 0.65, p < 0.001), vertical length (r = 0.57, p = 0.001), and depth (r = 0.46, p = 0.009). The mean values of all measurements were similar on the paired-samples t test: p = 0.9 for volume (30.86 ± 5.04 ml on CT and 30.88 ± 4.92 ml on MRI), p = 0.2 for horizontal length, p = 0.4 for vertical length, and p = 0.2 for depth. We present an innovative semiautomated method capable of calculating orbital volume and demonstrating orbital contour by MRI validated against the gold standard CT-based measurements. This method can serve as a valuable tool for evaluating diverse orbital processes.

Fully automated landmarking and facial segmentation on 3D photographs.

Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach. Ten landmarks were manually annotated on 2897 3D facial photographs. The automated landmarking workflow involved two successive DiffusionNet models. The dataset was randomly divided into a training and test dataset. The precision of the workflow was evaluated by calculating the Euclidean distances between the automated and manual landmarks and compared to the intra-observer and inter-observer variability of manual annotation and a semi-automated landmarking method. The workflow was successful in 98.6% of all test cases. The deep learning-based landmarking method achieved precise and consistent landmark annotation. The mean precision of 1.69 ± 1.15 mm was comparable to the inter-observer variability (1.31 ± 0.91 mm) of manual annotation. Automated landmark annotation on 3D photographs was achieved with the DiffusionNet-based approach. The proposed method allows quantitative analysis of large datasets and may be used in diagnosis, follow-up, and virtual surgical planning.

Modification of brain conductivity in human focal epilepsy: A model-based estimation from stereoelectroencephalography.

We have developed a novel method for estimating brain tissue electrical conductivity using low-intensity pulse stereoelectroencephalography (SEEG) stimulation coupled with biophysical modeling. We evaluated the hypothesis that brain conductivity is correlated with the degree of epileptogenicity in patients with drug-resistant focal epilepsy. We used bipolar low-intensity biphasic pulse stimulation (.2 mA) followed by a postprocessing pipeline for estimating brain conductivity. This processing is based on biophysical modeling of the electrical potential induced in brain tissue between the stimulated contacts in response to pulse stimulation. We estimated the degree of epileptogenicity using a semi-automatic method quantifying the dynamic of fast discharge at seizure onset: the epileptogenicity index (EI). We also investigated how the location of stimulation within specific anatomical brain regions or within lesional tissue impacts brain conductivity. We performed 1034 stimulations of 511 bipolar channels in 16 patients. We found that brain conductivity was lower in the epileptogenic zone (EZ; unpaired median difference = .064, p < .001) and inversely correlated with the epileptogenic index value (p < .001, Spearman rho = -.32). Conductivity values were also influenced by anatomical site, location within lesion, and delay between SEEG electrode implantation and stimulation, and had significant interpatient variability. Mixed model multivariate analysis showed that conductivity is significantly associated with EI (F = 13.45, p < .001), anatomical regions (F = 5.586, p < .001), delay since implantation (F = 14.71, p = .003), and age at SEEG (F = 6.591, p = .027), but not with the type of lesion (F = .372, p = .773) or the delay since last seizure (F = 1.592, p = .235). We provide a novel model-based method for estimating brain conductivity from SEEG low-intensity pulse stimulations. The brain tissue conductivity is lower in EZ as compared to non-EZ. Conductivity also varies significantly across anatomical brain regions. Involved pathophysiological processes may include changes in the extracellular space (especially volume or tortuosity) in epileptic tissue.

A semi-automated and high-throughput approach for the detection of honey bee viruses in bee samples.

Deformed wing virus (DWV) was first detected in dead honey bees in 1982 but has been in honey bees for at least 300 years. Due to its high prevalence and virulence, they have been linked with the ongoing decline in honey bee populations worldwide. A rapid, simple, semi-automated, high-throughput, and cost-effective method of screening colonies for viruses would benefit bee research and the beekeeping industry. Here we describe a semi-automated approach that combines an RNA-grade liquid homogenizer followed by magnetic bead capture for total virus nucleic acid extraction. We compare it to the more commonly applied nucleic acid column-based purification method and use qPCR plus Oxford Nanopore Technologies sequencing to evaluate the accuracy of analytical results for both methods. Our results showed high reproducibility and accuracy for both approaches. The semi-automated method described here allows for faster screening of viral loads in units of 96 samples at a time. We developed this method to monitor viral loads in honey bee colonies, but it could be easily applied for any PCR or genomic-based screening assays.

Enhancing deforestation monitoring in the Brazilian Amazon: A semi-automatic approach leveraging uncertainty estimation

Official governmental monitoring of deforestation in the Brazilian Amazon relies on human experts conducting visual analyzes of remote sensing images, an approach that is very expensive and time-consuming due to the enormous geographic extent to be inspected. The main obstacle to the adoption of fully automatic methods is the requirement for the highest possible deforestation detection accuracy, which arises from the official nature of the deforestation reports, used as a basis for deforestation prevention policies and international treaties. This work addresses such a high accuracy constraint by introducing a semi-automatic method that aims at limiting visual analysis to areas where automatic classification is characterized by high uncertainty. We hypothesize that classification uncertainty is correlated with classification error. Therefore, by trusting automatic detections with low uncertainty and subjecting areas of high uncertainty to visual audit, the amount of human intervention in the whole process could be limited to a small fraction of the forest area, while very high accuracies would be achieved. In this work we investigate various uncertainty estimation strategies, including multiple and single inference approaches, and evaluate different uncertainty metrics. Additionally, we conducted a comprehensive analysis to identify the natural factors and deforestation practices that contribute to high uncertainty in automatic classification. We draw interesting conclusions from the analysis of selected regions of two distinct target sites, which exemplify common causes of uncertainty and indicate how uncertainty may be related with ongoing forest degradation processes. Through experimental evaluation at those sites, we demonstrate that the proposed semi-automatic methodology can achieve impressive accuracies in terms of F1-score values, exceeding 97%, while reducing the total human audit workload to just 3% of the target area.

Semi-Automated Approach for Evaluation of Software Defect Management Process using ML Approach

Evaluation of the software development process is crucial for enhancing software production and product quality inside a company. Traditional methods that rely on manual qualitative evaluations (such as artifact inspection) are flawed because they are (i) time-consuming, (ii) hampered by authority limits, and (iii) frequently subjective. This research introduces a unique machine learning-based semi-automated method for software process assessment to get over these constraints. We specifically frame the issue as a sequence classification challenge that can be resolved using machine learning methods. We develop a new quantitative indicator to impartially assess the effectiveness and quality of a software process based on the framework. We use it to assess the defect management procedure used in four actual industrial software projects in order to verify the effectiveness of our methodology. Our empirical findings demonstrate the effectiveness and potential of our technique in offering a reliable, quantitative assessment of software process.

Quantification of myocardial scar of different etiology using dark- and bright-blood late gadolinium enhancement cardiovascular magnetic resonance

Dark-blood late gadolinium enhancement (LGE) has been shown to improve the visualization and quantification of areas of ischemic scar compared to standard bright-blood LGE. Recently, the performance of various semi-automated quantification methods has been evaluated for the assessment of infarct size using both dark-blood LGE and conventional bright-blood LGE with histopathology as a reference standard. However, the impact of this sequence on different quantification strategies in vivo remains uncertain. In this study, various semi-automated scar quantification methods were evaluated for a range of different ischemic and non-ischemic pathologies encountered in clinical practice. A total of 62 patients referred for clinical cardiovascular magnetic resonance (CMR) were retrospectively included. All patients had a confirmed diagnosis of either ischemic heart disease (IHD; n = 21), dilated/non-ischemic cardiomyopathy (NICM; n = 21), or hypertrophic cardiomyopathy (HCM; n = 20) and underwent CMR on a 1.5 T scanner including both bright- and dark-blood LGE using a standard PSIR sequence. Both methods used identical sequence settings as per clinical protocol, apart from the inversion time parameter, which was set differently. All short-axis LGE images with scar were manually segmented for epicardial and endocardial borders. The extent of LGE was then measured visually by manual signal thresholding, and semi-automatically by signal thresholding using the standard deviation (SD) and the full width at half maximum (FWHM) methods. For all quantification methods in the IHD group, except the 6 SD method, dark-blood LGE detected significantly more enhancement compared to bright-blood LGE (p < 0.05 for all methods). For both bright-blood and dark-blood LGE, the 6 SD method correlated best with manual thresholding (16.9% vs. 17.1% and 20.1% vs. 20.4%, respectively). For the NICM group, no significant differences between LGE methods were found. For bright-blood LGE, the 5 SD method agreed best with manual thresholding (9.3% vs. 11.0%), while for dark-blood LGE the 4 SD method agreed best (12.6% vs. 11.5%). Similarly, for the HCM group no significant differences between LGE methods were found. For bright-blood LGE, the 6 SD method agreed best with manual thresholding (10.9% vs. 12.2%), while for dark-blood LGE the 5 SD method agreed best (13.2% vs. 11.5%). Semi-automated LGE quantification using dark-blood LGE images is feasible in both patients with ischemic and non-ischemic scar patterns. Given the advantage in detecting scar in patients with ischemic heart disease and no disadvantage in patients with non-ischemic scar, dark-blood LGE can be readily and widely adopted into clinical practice without compromising on quantification.

Automatic and semi-automatic segmentation method of post-myocardial infarction according to magnetic resonance imaging with late gadolinium enhancement

Automatic and semi-automatic segmentation method of post-myocardial infarction according to magnetic resonance imaging with late gadolinium enhancement

X-ray tomography applied to tsunami deposits: Optimized image processing and quantitative analysis of particle size, particle shape, and sedimentary fabric in 3D

The analysis of particle size, shape, size, and their spatial arrangement (i.e. fabric) in a sedimentary deposit plays a significant role in their interpretation in terms of transport mechanism, flow conditions, and sediment source. The present work focuses on the use of X-ray computed tomography (X-CT) to analyze tsunami deposits (core samples), as examples of complex sedimentary assemblages. A workflow of image processing is proposed to measure 3D parameters of particle size, shape, and orientation using two different software packages (Fiji-ImageJ, and IPSDK) and two different methods of image segmentation. The semi-automated method provides better segmentation quality of the tsunami deposit, as compared with the poor performance of fully-automated image segmentation. We also provide some clues to reduce the computational time. This workflow is applied to two examples of tsunami deposits on the coasts of the Algarve (1755 Lisbon tsunami, southern Portugal). Optimized processing of X-CT images gives access to detailed vertical variations of grain size, grain shape, and componentry (clayey matrix, silicates, and carbonates, including marine bioclasts), as well as information on flow directions inferred from the sedimentary fabric in 3D. High-resolution analysis of the bedforms and vertical grain size grading allows determining the flow conditions during the inundation phase of the tsunami.